Affiliation:
1. Noorul Islam Centre for Higher Education, India
Abstract
Millions of people around the world have one or many respiratory-related illnesses. Many chronic respiratory diseases like asthma, COPD, pneumonia, respiratory distress, etc. are considered to be a significant public health burden. To reduce the mortality rate, it is better to perform early prediction of respiratory disorders and treat them accordingly. To build an efficient prediction model for various types of respiratory diseases, machine learning approaches are used. The proposed methodology builds classifier model using supervised learning algorithms like random forest, decision tree, and multi-layer perceptron neural network (MLP-NN) for the detection of different respiratory diseases of ICU admitted patients. It achieves accuracy of nearly 99% by various machine learning approaches.